Multi-Layer Transcriptome Analysis Uncovers Molecular Signatures of Coliform Mastitis in Lactating Cows

Bovine mastitis caused by Escherichia coli remains one of the most economically significant diseases in the dairy industry. This study performed a multi-layer transcriptome analysis of publicly available microarray data (GSE15025) derived from 15 German Holstein-Friesian heifers experimentally inoculated with E. coli, yielding 30 microarray samples across three GEO datasets (GSE15019, GSE15020, GSE15022) representing infected quarters at 6 h and 24 h post-inoculation and neighboring uninfected quarters at 24 h. Using an analytical framework combining WGCNA, differential gene expression profiling, PPI network construction, SVM classification, and computational miRNA target prediction, three significant co-expression modules were identified blue (367 genes, 6 h), turquoise (3602 genes, 24 h), and yellow (193 genes, 24 h neighboring tissue) alongside 21, 1570, and 250 differentially expressed genes respectively. Cross-referencing WGCNA and PPI analyses identified STAT3, JUN, and RAC2 as high-confidence hub gene candidates with the strongest convergent computational support. An SVM classifier achieved 87.5% accuracy (sensitivity = 100%, specificity = 75%) in distinguishing infected from control samples. Computational miRNA predictions suggested putative regulatory interactions requiring experimental confirmation. As a purely computational study, experimental validation remains a necessary future step; however, these findings provide a systematic characterization of candidate molecular mechanisms and potential biomarker targets for E. coli-induced bovine mastitis.